1) In machine learning, DR stands for dimensionality reduction. In machine learning, dimensionality reduction is a feature engineering technique, in which a large number of features in a dataset is reduced to a smaller number of features. It is important to ensure that the remaining features are meaningful and representative for the dataset and that only irrelevant and missing features are removed. Dimensionality reduction achieves the reduction of "dimensions" of a dataset, i.e. the reduction of the number of features which are present in the dataset.

A large number of features in a dataset can lead to the curse of dimensionality, according to which, certain problems start to arise in datasets with small number of examples and large number of features (dimensions). The common denominator in all these problems is that when the dataset number of features (dimensionality) increases, the volume of the space increases so fast that the available data in a dataset is deemed sparse. So the amount of data which is required for a highly accurate model in theory grows exponentially.

2) DR also stands for Disaster Recovery. It is a series of designs and policies which dictate how an information system is recovered from backup in case of a physical or logical disaster to the infrastructure, applications or data.

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